The second quantitative Consumer Duty outcome. Target-market alignment, utilisation and mis-selling risk — evidenced from your data.
Firms must demonstrate that products are designed for, and reaching, an identifiable target market — and that customers outside the target market are not being systematically sold products that will not serve them.
The expectation is evidence, not policy. Target-market statements sit at the front of the argument; the utilisation and outcome data sits behind them.
The engine ingests product taxonomy and customer usage data. It tests target-market alignment against the stated intended market, surfaces utilisation gaps that indicate poor fit, and flags mis-selling risk indicators for review.
The analytical approach is transparent, versioned and reproducible. Every finding traces back to the ingested data and the methodology version that produced it.
The engine surfaces customer cohorts where utilisation, outcomes or complaint patterns diverge from the target-market design. Outliers are ranked by severity and traced back to the transactions and product records that generated them, so remediation lands on evidence rather than assertion.
Consumer Duty explicitly covers the distribution chain. FairLedger carries product-outcome evidence through to the end customer per agent and per programme, so the board pack can answer whether products are serving customers across the chain — not only inside the manufacturer's own book.
Target-market alignment evidence, utilisation analysis by segment, mis-selling risk signals with severity ranking, distribution-chain outcome views, and a board-pack-ready summary the Consumer Duty Champion can present without translation.
Secure API or file-based ingestion, minimum-necessary data policy, tenant-isolated processing, immutable audit logging at every touch.
A discovery call runs 30 minutes and covers your Consumer Duty evidencing approach, the FairLedger fit and the design partner terms.
